Overview

Brought to you by YData

Dataset statistics

Number of variables30
Number of observations168
Missing cells1061
Missing cells (%)21.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory34.9 KiB
Average record size in memory212.8 B

Variable types

Categorical6
Numeric11
Boolean4
Text5
Unsupported4

Alerts

año is highly overall correlated with calidad_de_agua and 13 other fieldsHigh correlation
calidad_de_agua is highly overall correlated with año and 1 other fieldsHigh correlation
campaña is highly overall correlated with año and 3 other fieldsHigh correlation
clorofila_a_ug_l is highly overall correlated with año and 3 other fieldsHigh correlation
codigo is highly overall correlated with microcistina_ug_l and 1 other fieldsHigh correlation
color is highly overall correlated with espumas and 2 other fieldsHigh correlation
cr_total_mg_l is highly overall correlated with añoHigh correlation
dbo_mg_l is highly overall correlated with año and 2 other fieldsHigh correlation
dqo_mg_l is highly overall correlated with año and 2 other fieldsHigh correlation
espumas is highly overall correlated with color and 3 other fieldsHigh correlation
fecha is highly overall correlated with año and 3 other fieldsHigh correlation
ica is highly overall correlated with año and 2 other fieldsHigh correlation
mat_susp is highly overall correlated with color and 3 other fieldsHigh correlation
microcistina_ug_l is highly overall correlated with año and 7 other fieldsHigh correlation
od is highly overall correlated with año and 1 other fieldsHigh correlation
olores is highly overall correlated with color and 3 other fieldsHigh correlation
ph is highly overall correlated with año and 1 other fieldsHigh correlation
sitios is highly overall correlated with codigo and 1 other fieldsHigh correlation
tem_agua is highly overall correlated with año and 5 other fieldsHigh correlation
tem_aire is highly overall correlated with año and 3 other fieldsHigh correlation
turbiedad_ntu is highly overall correlated with añoHigh correlation
año is highly imbalanced (86.5%) Imbalance
tem_agua has 23 (13.7%) missing values Missing
tem_aire has 24 (14.3%) missing values Missing
od has 36 (21.4%) missing values Missing
ph has 56 (33.3%) missing values Missing
dbo_mg_l has 80 (47.6%) missing values Missing
dqo_mg_l has 72 (42.9%) missing values Missing
turbiedad_ntu has 19 (11.3%) missing values Missing
hidr_deriv_petr_ug_l has 168 (100.0%) missing values Missing
cr_total_mg_l has 131 (78.0%) missing values Missing
cd_total_mg_l has 168 (100.0%) missing values Missing
clorofila_a_ug_l has 92 (54.8%) missing values Missing
microcistina_ug_l has 161 (95.8%) missing values Missing
ica has 14 (8.3%) missing values Missing
calidad_de_agua has 14 (8.3%) missing values Missing
sitios is uniformly distributed Uniform
codigo is uniformly distributed Uniform
p_total_l_mg_l is an unsupported type, check if it needs cleaning or further analysis Unsupported
fosf_ortofos_mg_l is an unsupported type, check if it needs cleaning or further analysis Unsupported
hidr_deriv_petr_ug_l is an unsupported type, check if it needs cleaning or further analysis Unsupported
cd_total_mg_l is an unsupported type, check if it needs cleaning or further analysis Unsupported
clorofila_a_ug_l has 5 (3.0%) zeros Zeros

Reproduction

Analysis started2024-10-30 21:53:21.969473
Analysis finished2024-10-30 21:54:02.513636
Duration40.54 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

sitios
Categorical

High correlation  Uniform 

Distinct42
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
Canal Villanueva y Río Luján
 
4
Río Lujan y Arroyo Caraguatá
 
4
Canal Aliviador y Río Lujan
 
4
Río Carapachay y Arroyo Gallo Fiambre
 
4
Río Reconquista y Río Lujan
 
4
Other values (37)
148 

Length

Max length41
Median length27.5
Mean length23.238095
Min length8

Characters and Unicode

Total characters3904
Distinct characters57
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCanal Villanueva y Río Luján
2nd rowRío Lujan y Arroyo Caraguatá
3rd rowCanal Aliviador y Río Lujan
4th rowRío Carapachay y Arroyo Gallo Fiambre
5th rowRío Reconquista y Río Lujan

Common Values

ValueCountFrequency (%)
Canal Villanueva y Río Luján 4
 
2.4%
Río Lujan y Arroyo Caraguatá 4
 
2.4%
Canal Aliviador y Río Lujan 4
 
2.4%
Río Carapachay y Arroyo Gallo Fiambre 4
 
2.4%
Río Reconquista y Río Lujan 4
 
2.4%
Rio Tigre 100m antes del Rio Luján 4
 
2.4%
Río Lujan y Canal San Fernando 4
 
2.4%
Río Capitán y Río San Antonio 4
 
2.4%
Arroyo Abra Vieja y Santa Rosa 4
 
2.4%
Del Arca 4
 
2.4%
Other values (32) 128
76.2%

Length

2024-10-30T18:54:02.800825image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
y 40
 
5.9%
río 36
 
5.3%
de 32
 
4.7%
arroyo 24
 
3.5%
espigón 16
 
2.4%
lujan 16
 
2.4%
canal 12
 
1.8%
la 12
 
1.8%
playa 12
 
1.8%
reserva 12
 
1.8%
Other values (94) 468
68.8%

Most occurring characters

ValueCountFrequency (%)
512
 
13.1%
a 476
 
12.2%
o 312
 
8.0%
e 228
 
5.8%
r 224
 
5.7%
l 192
 
4.9%
n 192
 
4.9%
i 160
 
4.1%
s 120
 
3.1%
c 108
 
2.8%
Other values (47) 1380
35.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3904
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
512
 
13.1%
a 476
 
12.2%
o 312
 
8.0%
e 228
 
5.8%
r 224
 
5.7%
l 192
 
4.9%
n 192
 
4.9%
i 160
 
4.1%
s 120
 
3.1%
c 108
 
2.8%
Other values (47) 1380
35.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3904
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
512
 
13.1%
a 476
 
12.2%
o 312
 
8.0%
e 228
 
5.8%
r 224
 
5.7%
l 192
 
4.9%
n 192
 
4.9%
i 160
 
4.1%
s 120
 
3.1%
c 108
 
2.8%
Other values (47) 1380
35.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3904
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
512
 
13.1%
a 476
 
12.2%
o 312
 
8.0%
e 228
 
5.8%
r 224
 
5.7%
l 192
 
4.9%
n 192
 
4.9%
i 160
 
4.1%
s 120
 
3.1%
c 108
 
2.8%
Other values (47) 1380
35.3%

codigo
Categorical

High correlation  Uniform 

Distinct42
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
TI001
 
4
TI006
 
4
TI002
 
4
TI003
 
4
TI004
 
4
Other values (37)
148 

Length

Max length8
Median length5
Mean length5.0714286
Min length5

Characters and Unicode

Total characters852
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTI001
2nd rowTI006
3rd rowTI002
4th rowTI003
5th rowTI004

Common Values

ValueCountFrequency (%)
TI001 4
 
2.4%
TI006 4
 
2.4%
TI002 4
 
2.4%
TI003 4
 
2.4%
TI004 4
 
2.4%
TI005 4
 
2.4%
TI007 4
 
2.4%
TI008 4
 
2.4%
TI009 4
 
2.4%
SF015 4
 
2.4%
Other values (32) 128
76.2%

Length

2024-10-30T18:54:03.156467image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ti001 4
 
2.4%
ti006 4
 
2.4%
ti002 4
 
2.4%
ti003 4
 
2.4%
ti004 4
 
2.4%
ti005 4
 
2.4%
ti007 4
 
2.4%
ti008 4
 
2.4%
ti009 4
 
2.4%
sf015 4
 
2.4%
Other values (32) 128
76.2%

Most occurring characters

ValueCountFrequency (%)
0 204
23.9%
I 52
 
6.1%
2 44
 
5.2%
S 40
 
4.7%
4 40
 
4.7%
3 40
 
4.7%
T 36
 
4.2%
1 36
 
4.2%
5 36
 
4.2%
A 36
 
4.2%
Other values (20) 288
33.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 852
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 204
23.9%
I 52
 
6.1%
2 44
 
5.2%
S 40
 
4.7%
4 40
 
4.7%
3 40
 
4.7%
T 36
 
4.2%
1 36
 
4.2%
5 36
 
4.2%
A 36
 
4.2%
Other values (20) 288
33.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 852
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 204
23.9%
I 52
 
6.1%
2 44
 
5.2%
S 40
 
4.7%
4 40
 
4.7%
3 40
 
4.7%
T 36
 
4.2%
1 36
 
4.2%
5 36
 
4.2%
A 36
 
4.2%
Other values (20) 288
33.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 852
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 204
23.9%
I 52
 
6.1%
2 44
 
5.2%
S 40
 
4.7%
4 40
 
4.7%
3 40
 
4.7%
T 36
 
4.2%
1 36
 
4.2%
5 36
 
4.2%
A 36
 
4.2%
Other values (20) 288
33.8%

fecha
Categorical

High correlation 

Distinct7
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
23/2/2022
42 
4/5/2022
42 
23/8/2022
42 
31/10/2022
35 
no midieron este día
 
4
Other values (2)
 
3

Length

Max length20
Median length15.5
Mean length9.2440476
Min length8

Characters and Unicode

Total characters1553
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.6%

Sample

1st row23/2/2022
2nd row23/2/2022
3rd row23/2/2022
4th row23/2/2022
5th row23/2/2022

Common Values

ValueCountFrequency (%)
23/2/2022 42
25.0%
4/5/2022 42
25.0%
23/8/2022 42
25.0%
31/10/2022 35
20.8%
no midieron este día 4
 
2.4%
31/10/0202 2
 
1.2%
no se midió 1
 
0.6%

Length

2024-10-30T18:54:03.495358image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-30T18:54:03.864253image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
23/2/2022 42
23.1%
4/5/2022 42
23.1%
23/8/2022 42
23.1%
31/10/2022 35
19.2%
no 5
 
2.7%
midieron 4
 
2.2%
este 4
 
2.2%
día 4
 
2.2%
31/10/0202 2
 
1.1%
se 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
2 613
39.5%
/ 326
21.0%
0 202
 
13.0%
3 121
 
7.8%
1 74
 
4.8%
4 42
 
2.7%
5 42
 
2.7%
8 42
 
2.7%
14
 
0.9%
e 13
 
0.8%
Other values (11) 64
 
4.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1553
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 613
39.5%
/ 326
21.0%
0 202
 
13.0%
3 121
 
7.8%
1 74
 
4.8%
4 42
 
2.7%
5 42
 
2.7%
8 42
 
2.7%
14
 
0.9%
e 13
 
0.8%
Other values (11) 64
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1553
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 613
39.5%
/ 326
21.0%
0 202
 
13.0%
3 121
 
7.8%
1 74
 
4.8%
4 42
 
2.7%
5 42
 
2.7%
8 42
 
2.7%
14
 
0.9%
e 13
 
0.8%
Other values (11) 64
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1553
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 613
39.5%
/ 326
21.0%
0 202
 
13.0%
3 121
 
7.8%
1 74
 
4.8%
4 42
 
2.7%
5 42
 
2.7%
8 42
 
2.7%
14
 
0.9%
e 13
 
0.8%
Other values (11) 64
 
4.1%

año
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2022
163 
no midieron este día
 
4
no se midió
 
1

Length

Max length20
Median length4
Mean length4.422619
Min length4

Characters and Unicode

Total characters743
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.6%

Sample

1st row2022
2nd row2022
3rd row2022
4th row2022
5th row2022

Common Values

ValueCountFrequency (%)
2022 163
97.0%
no midieron este día 4
 
2.4%
no se midió 1
 
0.6%

Length

2024-10-30T18:54:04.290748image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-30T18:54:04.694054image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
2022 163
89.6%
no 5
 
2.7%
midieron 4
 
2.2%
este 4
 
2.2%
día 4
 
2.2%
se 1
 
0.5%
midió 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
2 489
65.8%
0 163
 
21.9%
14
 
1.9%
e 13
 
1.7%
i 10
 
1.3%
o 9
 
1.2%
n 9
 
1.2%
d 9
 
1.2%
m 5
 
0.7%
s 5
 
0.7%
Other values (5) 17
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 743
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 489
65.8%
0 163
 
21.9%
14
 
1.9%
e 13
 
1.7%
i 10
 
1.3%
o 9
 
1.2%
n 9
 
1.2%
d 9
 
1.2%
m 5
 
0.7%
s 5
 
0.7%
Other values (5) 17
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 743
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 489
65.8%
0 163
 
21.9%
14
 
1.9%
e 13
 
1.7%
i 10
 
1.3%
o 9
 
1.2%
n 9
 
1.2%
d 9
 
1.2%
m 5
 
0.7%
s 5
 
0.7%
Other values (5) 17
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 743
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 489
65.8%
0 163
 
21.9%
14
 
1.9%
e 13
 
1.7%
i 10
 
1.3%
o 9
 
1.2%
n 9
 
1.2%
d 9
 
1.2%
m 5
 
0.7%
s 5
 
0.7%
Other values (5) 17
 
2.3%

campaña
Categorical

High correlation 

Distinct6
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
Verano
42 
otoño
42 
invierno
42 
Primavera
37 
no midieron este día
 
4

Length

Max length20
Median length10
Mean length7.2738095
Min length5

Characters and Unicode

Total characters1222
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.6%

Sample

1st rowVerano
2nd rowVerano
3rd rowVerano
4th rowVerano
5th rowVerano

Common Values

ValueCountFrequency (%)
Verano 42
25.0%
otoño 42
25.0%
invierno 42
25.0%
Primavera 37
22.0%
no midieron este día 4
 
2.4%
no se midió 1
 
0.6%

Length

2024-10-30T18:54:05.107054image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-30T18:54:05.473552image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
verano 42
23.1%
otoño 42
23.1%
invierno 42
23.1%
primavera 37
20.3%
no 5
 
2.7%
midieron 4
 
2.2%
este 4
 
2.2%
día 4
 
2.2%
se 1
 
0.5%
midió 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
o 219
17.9%
r 162
13.3%
n 135
11.0%
e 134
11.0%
i 131
10.7%
a 120
9.8%
v 79
 
6.5%
t 46
 
3.8%
V 42
 
3.4%
ñ 42
 
3.4%
Other values (7) 112
9.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1222
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 219
17.9%
r 162
13.3%
n 135
11.0%
e 134
11.0%
i 131
10.7%
a 120
9.8%
v 79
 
6.5%
t 46
 
3.8%
V 42
 
3.4%
ñ 42
 
3.4%
Other values (7) 112
9.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1222
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 219
17.9%
r 162
13.3%
n 135
11.0%
e 134
11.0%
i 131
10.7%
a 120
9.8%
v 79
 
6.5%
t 46
 
3.8%
V 42
 
3.4%
ñ 42
 
3.4%
Other values (7) 112
9.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1222
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 219
17.9%
r 162
13.3%
n 135
11.0%
e 134
11.0%
i 131
10.7%
a 120
9.8%
v 79
 
6.5%
t 46
 
3.8%
V 42
 
3.4%
ñ 42
 
3.4%
Other values (7) 112
9.2%

tem_agua
Real number (ℝ)

High correlation  Missing 

Distinct91
Distinct (%)62.8%
Missing23
Missing (%)13.7%
Infinite0
Infinite (%)0.0%
Mean17.823655
Minimum6
Maximum27.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-10-30T18:54:06.008454image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile10
Q114.6
median17.9
Q320.4
95-th percentile25.78
Maximum27.4
Range21.4
Interquartile range (IQR)5.8

Descriptive statistics

Standard deviation4.8486881
Coefficient of variation (CV)0.27203669
Kurtosis-0.40385925
Mean17.823655
Median Absolute Deviation (MAD)3
Skewness-0.073431926
Sum2584.43
Variance23.509776
MonotonicityNot monotonic
2024-10-30T18:54:06.613114image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 7
 
4.2%
20 6
 
3.6%
18.6 5
 
3.0%
18.5 5
 
3.0%
17 4
 
2.4%
23 3
 
1.8%
17.1 3
 
1.8%
15.6 3
 
1.8%
24.7 3
 
1.8%
18.2 3
 
1.8%
Other values (81) 103
61.3%
(Missing) 23
 
13.7%
ValueCountFrequency (%)
6 1
 
0.6%
7 2
 
1.2%
8 2
 
1.2%
9 1
 
0.6%
10 7
4.2%
10.01 1
 
0.6%
11 1
 
0.6%
11.01 1
 
0.6%
12 1
 
0.6%
12.7 2
 
1.2%
ValueCountFrequency (%)
27.4 1
0.6%
27 1
0.6%
26.5 1
0.6%
26.3 1
0.6%
26.1 2
1.2%
26 1
0.6%
25.8 1
0.6%
25.7 1
0.6%
25.4 2
1.2%
25.2 1
0.6%

tem_aire
Real number (ℝ)

High correlation  Missing 

Distinct29
Distinct (%)20.1%
Missing24
Missing (%)14.3%
Infinite0
Infinite (%)0.0%
Mean15.793056
Minimum4
Maximum27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-10-30T18:54:07.049369image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile8
Q113
median14
Q319
95-th percentile25.2
Maximum27
Range23
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.1243509
Coefficient of variation (CV)0.32446862
Kurtosis-0.25796949
Mean15.793056
Median Absolute Deviation (MAD)2
Skewness0.47535054
Sum2274.2
Variance26.258972
MonotonicityNot monotonic
2024-10-30T18:54:07.424659image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
14 25
14.9%
13 15
 
8.9%
12 12
 
7.1%
16 11
 
6.5%
17 9
 
5.4%
15 7
 
4.2%
27 6
 
3.6%
22 6
 
3.6%
8 5
 
3.0%
23 5
 
3.0%
Other values (19) 43
25.6%
(Missing) 24
14.3%
ValueCountFrequency (%)
4 1
 
0.6%
5 1
 
0.6%
6 1
 
0.6%
7 1
 
0.6%
8 5
3.0%
9 3
 
1.8%
10 5
3.0%
11 4
 
2.4%
12 12
7.1%
12.3 1
 
0.6%
ValueCountFrequency (%)
27 6
3.6%
26 1
 
0.6%
25.2 3
1.8%
25 1
 
0.6%
23.3 5
3.0%
23 5
3.0%
22.2 2
 
1.2%
22 6
3.6%
21 2
 
1.2%
20 4
2.4%

od
Real number (ℝ)

High correlation  Missing 

Distinct126
Distinct (%)95.5%
Missing36
Missing (%)21.4%
Infinite0
Infinite (%)0.0%
Mean6.7473485
Minimum0.36
Maximum17.61
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-10-30T18:54:07.852426image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.36
5-th percentile2.0035
Q15.065
median6.65
Q38.58
95-th percentile10.943
Maximum17.61
Range17.25
Interquartile range (IQR)3.515

Descriptive statistics

Standard deviation2.8357637
Coefficient of variation (CV)0.42027823
Kurtosis0.85228892
Mean6.7473485
Median Absolute Deviation (MAD)1.74
Skewness0.2222464
Sum890.65
Variance8.0415555
MonotonicityNot monotonic
2024-10-30T18:54:08.305370image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.3 2
 
1.2%
4.28 2
 
1.2%
7.85 2
 
1.2%
9 2
 
1.2%
7 2
 
1.2%
5.36 2
 
1.2%
1.5 1
 
0.6%
6.3 1
 
0.6%
4.49 1
 
0.6%
3.85 1
 
0.6%
Other values (116) 116
69.0%
(Missing) 36
 
21.4%
ValueCountFrequency (%)
0.36 1
0.6%
0.45 1
0.6%
1.02 1
0.6%
1.13 1
0.6%
1.39 1
0.6%
1.5 1
0.6%
1.8 1
0.6%
2.17 1
0.6%
2.22 1
0.6%
2.25 1
0.6%
ValueCountFrequency (%)
17.61 1
0.6%
12.84 1
0.6%
12.15 1
0.6%
12 1
0.6%
11.82 1
0.6%
11.05 1
0.6%
11.02 1
0.6%
10.88 1
0.6%
10.83 1
0.6%
10.6 1
0.6%

ph
Real number (ℝ)

High correlation  Missing 

Distinct91
Distinct (%)81.2%
Missing56
Missing (%)33.3%
Infinite0
Infinite (%)0.0%
Mean7.57
Minimum5
Maximum10.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-10-30T18:54:08.864822image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile6.5355
Q17.075
median7.485
Q38.0025
95-th percentile8.884
Maximum10.02
Range5.02
Interquartile range (IQR)0.9275

Descriptive statistics

Standard deviation0.7703632
Coefficient of variation (CV)0.10176528
Kurtosis1.5189961
Mean7.57
Median Absolute Deviation (MAD)0.475
Skewness0.42013799
Sum847.84
Variance0.59345946
MonotonicityNot monotonic
2024-10-30T18:54:09.433889image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.4 6
 
3.6%
7.6 4
 
2.4%
7.76 3
 
1.8%
7.3 3
 
1.8%
7.99 3
 
1.8%
7.5 2
 
1.2%
7.8 2
 
1.2%
7.12 2
 
1.2%
8.11 2
 
1.2%
6.76 2
 
1.2%
Other values (81) 83
49.4%
(Missing) 56
33.3%
ValueCountFrequency (%)
5 1
0.6%
6.2 1
0.6%
6.37 1
0.6%
6.39 1
0.6%
6.48 1
0.6%
6.53 1
0.6%
6.54 1
0.6%
6.56 2
1.2%
6.59 1
0.6%
6.66 1
0.6%
ValueCountFrequency (%)
10.02 1
0.6%
9.98 1
0.6%
9.39 1
0.6%
9.16 1
0.6%
9.01 1
0.6%
8.95 1
0.6%
8.83 1
0.6%
8.81 1
0.6%
8.62 1
0.6%
8.59 1
0.6%

olores
Boolean

High correlation 

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size300.0 B
False
120 
True
48 
ValueCountFrequency (%)
False 120
71.4%
True 48
 
28.6%
2024-10-30T18:54:09.755198image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

color
Boolean

High correlation 

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size300.0 B
False
119 
True
49 
ValueCountFrequency (%)
False 119
70.8%
True 49
29.2%
2024-10-30T18:54:10.001027image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

espumas
Boolean

High correlation 

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size300.0 B
False
127 
True
41 
ValueCountFrequency (%)
False 127
75.6%
True 41
 
24.4%
2024-10-30T18:54:10.262694image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

mat_susp
Boolean

High correlation 

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size300.0 B
False
106 
True
62 
ValueCountFrequency (%)
False 106
63.1%
True 62
36.9%
2024-10-30T18:54:10.522803image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Distinct90
Distinct (%)53.9%
Missing1
Missing (%)0.6%
Memory size1.4 KiB
2024-10-30T18:54:11.229034image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length20
Median length11
Mean length5.1197605
Min length2

Characters and Unicode

Total characters855
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique52 ?
Unique (%)31.1%

Sample

1st row2200
2nd row1200
3rd row1800
4th row1400
5th row1100
ValueCountFrequency (%)
no 14
 
7.0%
se 10
 
5.0%
midió 10
 
5.0%
20000 6
 
3.0%
1400 5
 
2.5%
1000 5
 
2.5%
1800 5
 
2.5%
900 4
 
2.0%
3000 4
 
2.0%
40000 4
 
2.0%
Other values (84) 132
66.3%
2024-10-30T18:54:12.368775image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 420
49.1%
1 57
 
6.7%
2 45
 
5.3%
32
 
3.7%
6 28
 
3.3%
i 28
 
3.3%
4 25
 
2.9%
5 23
 
2.7%
e 22
 
2.6%
3 22
 
2.6%
Other values (13) 153
 
17.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 855
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 420
49.1%
1 57
 
6.7%
2 45
 
5.3%
32
 
3.7%
6 28
 
3.3%
i 28
 
3.3%
4 25
 
2.9%
5 23
 
2.7%
e 22
 
2.6%
3 22
 
2.6%
Other values (13) 153
 
17.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 855
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 420
49.1%
1 57
 
6.7%
2 45
 
5.3%
32
 
3.7%
6 28
 
3.3%
i 28
 
3.3%
4 25
 
2.9%
5 23
 
2.7%
e 22
 
2.6%
3 22
 
2.6%
Other values (13) 153
 
17.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 855
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 420
49.1%
1 57
 
6.7%
2 45
 
5.3%
32
 
3.7%
6 28
 
3.3%
i 28
 
3.3%
4 25
 
2.9%
5 23
 
2.7%
e 22
 
2.6%
3 22
 
2.6%
Other values (13) 153
 
17.9%
Distinct79
Distinct (%)47.3%
Missing1
Missing (%)0.6%
Memory size1.4 KiB
2024-10-30T18:54:13.044664image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length20
Median length11
Mean length4.0239521
Min length1

Characters and Unicode

Total characters672
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50 ?
Unique (%)29.9%

Sample

1st row100
2nd row200
3rd row200
4th row100
5th row100
ValueCountFrequency (%)
100 17
 
8.5%
200 15
 
7.5%
no 14
 
7.0%
se 10
 
5.0%
midió 10
 
5.0%
300 6
 
3.0%
600 5
 
2.5%
500 4
 
2.0%
1000 4
 
2.0%
10000 4
 
2.0%
Other values (73) 110
55.3%
2024-10-30T18:54:13.985559image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 273
40.6%
1 55
 
8.2%
2 36
 
5.4%
32
 
4.8%
i 28
 
4.2%
3 28
 
4.2%
5 25
 
3.7%
6 24
 
3.6%
e 22
 
3.3%
n 18
 
2.7%
Other values (13) 131
19.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 672
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 273
40.6%
1 55
 
8.2%
2 36
 
5.4%
32
 
4.8%
i 28
 
4.2%
3 28
 
4.2%
5 25
 
3.7%
6 24
 
3.6%
e 22
 
3.3%
n 18
 
2.7%
Other values (13) 131
19.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 672
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 273
40.6%
1 55
 
8.2%
2 36
 
5.4%
32
 
4.8%
i 28
 
4.2%
3 28
 
4.2%
5 25
 
3.7%
6 24
 
3.6%
e 22
 
3.3%
n 18
 
2.7%
Other values (13) 131
19.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 672
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 273
40.6%
1 55
 
8.2%
2 36
 
5.4%
32
 
4.8%
i 28
 
4.2%
3 28
 
4.2%
5 25
 
3.7%
6 24
 
3.6%
e 22
 
3.3%
n 18
 
2.7%
Other values (13) 131
19.5%
Distinct86
Distinct (%)51.5%
Missing1
Missing (%)0.6%
Memory size1.4 KiB
2024-10-30T18:54:14.826543image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length20
Median length3
Mean length3.7305389
Min length1

Characters and Unicode

Total characters623
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique49 ?
Unique (%)29.3%

Sample

1st row130
2nd row400
3rd row580
4th row300
5th row370
ValueCountFrequency (%)
no 14
 
7.0%
se 10
 
5.0%
midió 10
 
5.0%
100 7
 
3.5%
300 6
 
3.0%
10 6
 
3.0%
50 6
 
3.0%
1500 5
 
2.5%
20 5
 
2.5%
2 5
 
2.5%
Other values (80) 125
62.8%
2024-10-30T18:54:15.892116image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 187
30.0%
1 53
 
8.5%
5 38
 
6.1%
2 36
 
5.8%
32
 
5.1%
3 29
 
4.7%
i 28
 
4.5%
4 26
 
4.2%
e 22
 
3.5%
6 21
 
3.4%
Other values (13) 151
24.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 623
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 187
30.0%
1 53
 
8.5%
5 38
 
6.1%
2 36
 
5.8%
32
 
5.1%
3 29
 
4.7%
i 28
 
4.5%
4 26
 
4.2%
e 22
 
3.5%
6 21
 
3.4%
Other values (13) 151
24.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 623
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 187
30.0%
1 53
 
8.5%
5 38
 
6.1%
2 36
 
5.8%
32
 
5.1%
3 29
 
4.7%
i 28
 
4.5%
4 26
 
4.2%
e 22
 
3.5%
6 21
 
3.4%
Other values (13) 151
24.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 623
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 187
30.0%
1 53
 
8.5%
5 38
 
6.1%
2 36
 
5.8%
32
 
5.1%
3 29
 
4.7%
i 28
 
4.5%
4 26
 
4.2%
e 22
 
3.5%
6 21
 
3.4%
Other values (13) 151
24.2%
Distinct87
Distinct (%)51.8%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2024-10-30T18:54:16.601312image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length20
Median length3
Mean length3.9404762
Min length1

Characters and Unicode

Total characters662
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39 ?
Unique (%)23.2%

Sample

1st row2.9
2nd row3.3
3rd row6.5
4th row7.4
5th row8.8
ValueCountFrequency (%)
no 14
 
7.0%
se 10
 
5.0%
midió 10
 
5.0%
3.3 5
 
2.5%
1.9 4
 
2.0%
5.1 4
 
2.0%
5.9 4
 
2.0%
3.7 4
 
2.0%
3.9 4
 
2.0%
midieron 4
 
2.0%
Other values (81) 137
68.5%
2024-10-30T18:54:17.801959image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 141
21.3%
1 60
 
9.1%
2 47
 
7.1%
3 46
 
6.9%
6 33
 
5.0%
32
 
4.8%
5 31
 
4.7%
4 30
 
4.5%
i 28
 
4.2%
8 25
 
3.8%
Other values (15) 189
28.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 662
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 141
21.3%
1 60
 
9.1%
2 47
 
7.1%
3 46
 
6.9%
6 33
 
5.0%
32
 
4.8%
5 31
 
4.7%
4 30
 
4.5%
i 28
 
4.2%
8 25
 
3.8%
Other values (15) 189
28.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 662
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 141
21.3%
1 60
 
9.1%
2 47
 
7.1%
3 46
 
6.9%
6 33
 
5.0%
32
 
4.8%
5 31
 
4.7%
4 30
 
4.5%
i 28
 
4.2%
8 25
 
3.8%
Other values (15) 189
28.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 662
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 141
21.3%
1 60
 
9.1%
2 47
 
7.1%
3 46
 
6.9%
6 33
 
5.0%
32
 
4.8%
5 31
 
4.7%
4 30
 
4.5%
i 28
 
4.2%
8 25
 
3.8%
Other values (15) 189
28.5%
Distinct87
Distinct (%)51.8%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2024-10-30T18:54:18.498421image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length20
Median length11
Mean length4.422619
Min length1

Characters and Unicode

Total characters743
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique54 ?
Unique (%)32.1%

Sample

1st row0.42
2nd row0.51
3rd row0.05
4th row1
5th row0.049
ValueCountFrequency (%)
0.05 18
 
9.0%
no 14
 
7.0%
0.049 12
 
6.0%
se 10
 
5.0%
midió 10
 
5.0%
0.1 5
 
2.5%
0.41 5
 
2.5%
2 5
 
2.5%
1 4
 
2.0%
midieron 4
 
2.0%
Other values (80) 113
56.5%
2024-10-30T18:54:19.417382image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 138
18.6%
0 134
18.0%
1 54
 
7.3%
5 41
 
5.5%
2 36
 
4.8%
4 34
 
4.6%
32
 
4.3%
9 30
 
4.0%
i 28
 
3.8%
3 24
 
3.2%
Other values (15) 192
25.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 743
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 138
18.6%
0 134
18.0%
1 54
 
7.3%
5 41
 
5.5%
2 36
 
4.8%
4 34
 
4.6%
32
 
4.3%
9 30
 
4.0%
i 28
 
3.8%
3 24
 
3.2%
Other values (15) 192
25.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 743
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 138
18.6%
0 134
18.0%
1 54
 
7.3%
5 41
 
5.5%
2 36
 
4.8%
4 34
 
4.6%
32
 
4.3%
9 30
 
4.0%
i 28
 
3.8%
3 24
 
3.2%
Other values (15) 192
25.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 743
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 138
18.6%
0 134
18.0%
1 54
 
7.3%
5 41
 
5.5%
2 36
 
4.8%
4 34
 
4.6%
32
 
4.3%
9 30
 
4.0%
i 28
 
3.8%
3 24
 
3.2%
Other values (15) 192
25.8%

p_total_l_mg_l
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size1.4 KiB

fosf_ortofos_mg_l
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size1.4 KiB

dbo_mg_l
Real number (ℝ)

High correlation  Missing 

Distinct51
Distinct (%)58.0%
Missing80
Missing (%)47.6%
Infinite0
Infinite (%)0.0%
Mean7.0659091
Minimum1.9
Maximum42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-10-30T18:54:19.794152image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.9
5-th percentile1.9
Q13.5
median5.55
Q39.325
95-th percentile15.65
Maximum42
Range40.1
Interquartile range (IQR)5.825

Descriptive statistics

Standard deviation5.6715439
Coefficient of variation (CV)0.80266302
Kurtosis15.813389
Mean7.0659091
Median Absolute Deviation (MAD)2.6
Skewness3.114005
Sum621.8
Variance32.166411
MonotonicityNot monotonic
2024-10-30T18:54:20.211849image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.9 10
 
6.0%
12 5
 
3.0%
5.8 3
 
1.8%
14 3
 
1.8%
11 3
 
1.8%
5.2 3
 
1.8%
5 3
 
1.8%
9.4 3
 
1.8%
6.5 3
 
1.8%
3.5 2
 
1.2%
Other values (41) 50
29.8%
(Missing) 80
47.6%
ValueCountFrequency (%)
1.9 10
6.0%
2 1
 
0.6%
2.3 1
 
0.6%
2.4 2
 
1.2%
2.5 2
 
1.2%
2.6 1
 
0.6%
2.8 1
 
0.6%
3.1 1
 
0.6%
3.3 1
 
0.6%
3.4 1
 
0.6%
ValueCountFrequency (%)
42 1
 
0.6%
21 1
 
0.6%
18 2
 
1.2%
16 1
 
0.6%
15 1
 
0.6%
14 3
1.8%
12 5
3.0%
11 3
1.8%
10 2
 
1.2%
9.4 3
1.8%

dqo_mg_l
Real number (ℝ)

High correlation  Missing 

Distinct47
Distinct (%)49.0%
Missing72
Missing (%)42.9%
Infinite0
Infinite (%)0.0%
Mean51.302083
Minimum29
Maximum180
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-10-30T18:54:20.657072image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum29
5-th percentile29
Q129
median41.5
Q364.25
95-th percentile91
Maximum180
Range151
Interquartile range (IQR)35.25

Descriptive statistics

Standard deviation26.944043
Coefficient of variation (CV)0.52520369
Kurtosis5.3700302
Mean51.302083
Median Absolute Deviation (MAD)12.5
Skewness1.9123341
Sum4925
Variance725.98147
MonotonicityNot monotonic
2024-10-30T18:54:21.029073image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
29 27
 
16.1%
39 4
 
2.4%
48 3
 
1.8%
33 3
 
1.8%
30 3
 
1.8%
36 3
 
1.8%
46 2
 
1.2%
34 2
 
1.2%
63 2
 
1.2%
54 2
 
1.2%
Other values (37) 45
26.8%
(Missing) 72
42.9%
ValueCountFrequency (%)
29 27
16.1%
30 3
 
1.8%
31 1
 
0.6%
32 2
 
1.2%
33 3
 
1.8%
34 2
 
1.2%
35 1
 
0.6%
36 3
 
1.8%
37 1
 
0.6%
39 4
 
2.4%
ValueCountFrequency (%)
180 1
0.6%
135 1
0.6%
130 1
0.6%
110 1
0.6%
94 1
0.6%
90 1
0.6%
89 1
0.6%
88 1
0.6%
84 1
0.6%
82 2
1.2%

turbiedad_ntu
Real number (ℝ)

High correlation  Missing 

Distinct56
Distinct (%)37.6%
Missing19
Missing (%)11.3%
Infinite0
Infinite (%)0.0%
Mean35.240268
Minimum2.5
Maximum130
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-10-30T18:54:21.418660image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2.5
5-th percentile10.4
Q118
median28
Q345
95-th percentile85
Maximum130
Range127.5
Interquartile range (IQR)27

Descriptive statistics

Standard deviation24.106259
Coefficient of variation (CV)0.68405437
Kurtosis1.4221911
Mean35.240268
Median Absolute Deviation (MAD)11
Skewness1.3000315
Sum5250.8
Variance581.11175
MonotonicityNot monotonic
2024-10-30T18:54:21.792210image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 7
 
4.2%
45 6
 
3.6%
19 6
 
3.6%
22 6
 
3.6%
90 5
 
3.0%
17 5
 
3.0%
28 5
 
3.0%
23 5
 
3.0%
26 5
 
3.0%
13 5
 
3.0%
Other values (46) 94
56.0%
(Missing) 19
 
11.3%
ValueCountFrequency (%)
2.5 1
 
0.6%
3.3 1
 
0.6%
4.1 1
 
0.6%
6 1
 
0.6%
7.5 1
 
0.6%
8.9 1
 
0.6%
9.3 1
 
0.6%
10 1
 
0.6%
11 2
 
1.2%
12 7
4.2%
ValueCountFrequency (%)
130 1
 
0.6%
110 1
 
0.6%
90 5
3.0%
85 2
 
1.2%
84 1
 
0.6%
80 2
 
1.2%
75 3
1.8%
71 1
 
0.6%
70 3
1.8%
67 1
 
0.6%

hidr_deriv_petr_ug_l
Unsupported

Missing  Rejected  Unsupported 

Missing168
Missing (%)100.0%
Memory size1.4 KiB

cr_total_mg_l
Real number (ℝ)

High correlation  Missing 

Distinct23
Distinct (%)62.2%
Missing131
Missing (%)78.0%
Infinite0
Infinite (%)0.0%
Mean2.1137676
Minimum0.005
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-10-30T18:54:22.193955image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.005
5-th percentile0.00508
Q10.0064
median0.0082
Q35
95-th percentile8.4
Maximum12
Range11.995
Interquartile range (IQR)4.9936

Descriptive statistics

Standard deviation3.474852
Coefficient of variation (CV)1.643914
Kurtosis0.63241462
Mean2.1137676
Median Absolute Deviation (MAD)0.0022
Skewness1.3536248
Sum78.2094
Variance12.074596
MonotonicityNot monotonic
2024-10-30T18:54:22.518263image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0.007 5
 
3.0%
6 4
 
2.4%
0.006 3
 
1.8%
0.011 2
 
1.2%
7 2
 
1.2%
0.0061 2
 
1.2%
0.005 2
 
1.2%
5 2
 
1.2%
0.009 1
 
0.6%
0.01 1
 
0.6%
Other values (13) 13
 
7.7%
(Missing) 131
78.0%
ValueCountFrequency (%)
0.005 2
 
1.2%
0.0051 1
 
0.6%
0.006 3
1.8%
0.0061 2
 
1.2%
0.0062 1
 
0.6%
0.0064 1
 
0.6%
0.0069 1
 
0.6%
0.007 5
3.0%
0.0079 1
 
0.6%
0.008 1
 
0.6%
ValueCountFrequency (%)
12 1
 
0.6%
10 1
 
0.6%
8 1
 
0.6%
7 2
1.2%
6 4
2.4%
5 2
1.2%
0.02 1
 
0.6%
0.015 1
 
0.6%
0.011 2
1.2%
0.01 1
 
0.6%

cd_total_mg_l
Unsupported

Missing  Rejected  Unsupported 

Missing168
Missing (%)100.0%
Memory size1.4 KiB

clorofila_a_ug_l
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct67
Distinct (%)88.2%
Missing92
Missing (%)54.8%
Infinite0
Infinite (%)0.0%
Mean524.40263
Minimum0
Maximum6410
Zeros5
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-10-30T18:54:22.843732image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.85
median45.05
Q3451.1
95-th percentile2600
Maximum6410
Range6410
Interquartile range (IQR)448.25

Descriptive statistics

Standard deviation1106.7598
Coefficient of variation (CV)2.1105154
Kurtosis12.687999
Mean524.40263
Median Absolute Deviation (MAD)44.9
Skewness3.3179342
Sum39854.6
Variance1224917.3
MonotonicityNot monotonic
2024-10-30T18:54:23.177958image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5
 
3.0%
0.3 3
 
1.8%
350 2
 
1.2%
0.2 2
 
1.2%
0.6 2
 
1.2%
70.8 1
 
0.6%
20.7 1
 
0.6%
130.2 1
 
0.6%
42.1 1
 
0.6%
140.8 1
 
0.6%
Other values (57) 57
33.9%
(Missing) 92
54.8%
ValueCountFrequency (%)
0 5
3.0%
0.1 1
 
0.6%
0.2 2
 
1.2%
0.3 3
1.8%
0.4 1
 
0.6%
0.5 1
 
0.6%
0.6 2
 
1.2%
0.7 1
 
0.6%
0.8 1
 
0.6%
1 1
 
0.6%
ValueCountFrequency (%)
6410 1
0.6%
4650 1
0.6%
3590 1
0.6%
2900 1
0.6%
2500 1
0.6%
2130 1
0.6%
1960 1
0.6%
1730 1
0.6%
1400 1
0.6%
1290 1
0.6%

microcistina_ug_l
Real number (ℝ)

High correlation  Missing 

Distinct6
Distinct (%)85.7%
Missing161
Missing (%)95.8%
Infinite0
Infinite (%)0.0%
Mean0.68
Minimum0.19
Maximum1.67
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-10-30T18:54:23.462273image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.19
5-th percentile0.193
Q10.25
median0.4
Q31
95-th percentile1.469
Maximum1.67
Range1.48
Interquartile range (IQR)0.75

Descriptive statistics

Standard deviation0.55949382
Coefficient of variation (CV)0.82278503
Kurtosis-0.069460209
Mean0.68
Median Absolute Deviation (MAD)0.21
Skewness0.97357994
Sum4.76
Variance0.31303333
MonotonicityNot monotonic
2024-10-30T18:54:23.734950image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 2
 
1.2%
0.2 1
 
0.6%
0.4 1
 
0.6%
0.3 1
 
0.6%
1.67 1
 
0.6%
0.19 1
 
0.6%
(Missing) 161
95.8%
ValueCountFrequency (%)
0.19 1
0.6%
0.2 1
0.6%
0.3 1
0.6%
0.4 1
0.6%
1 2
1.2%
1.67 1
0.6%
ValueCountFrequency (%)
1.67 1
0.6%
1 2
1.2%
0.4 1
0.6%
0.3 1
0.6%
0.2 1
0.6%
0.19 1
0.6%

ica
Real number (ℝ)

High correlation  Missing 

Distinct38
Distinct (%)24.7%
Missing14
Missing (%)8.3%
Infinite0
Infinite (%)0.0%
Mean44.071429
Minimum23
Maximum76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-10-30T18:54:24.028985image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum23
5-th percentile33
Q138
median42
Q350
95-th percentile59.35
Maximum76
Range53
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.9487335
Coefficient of variation (CV)0.20305068
Kurtosis0.32568788
Mean44.071429
Median Absolute Deviation (MAD)5
Skewness0.65752044
Sum6787
Variance80.079832
MonotonicityNot monotonic
2024-10-30T18:54:24.348124image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
40 13
 
7.7%
42 10
 
6.0%
37 10
 
6.0%
36 9
 
5.4%
38 8
 
4.8%
45 7
 
4.2%
39 7
 
4.2%
41 7
 
4.2%
46 7
 
4.2%
55 6
 
3.6%
Other values (28) 70
41.7%
(Missing) 14
 
8.3%
ValueCountFrequency (%)
23 1
 
0.6%
25 1
 
0.6%
29 2
 
1.2%
31 1
 
0.6%
32 2
 
1.2%
33 4
 
2.4%
34 3
 
1.8%
35 5
3.0%
36 9
5.4%
37 10
6.0%
ValueCountFrequency (%)
76 1
 
0.6%
67 1
 
0.6%
64 1
 
0.6%
62 1
 
0.6%
61 2
 
1.2%
60 2
 
1.2%
59 5
3.0%
58 4
2.4%
57 1
 
0.6%
56 2
 
1.2%

calidad_de_agua
Categorical

High correlation  Missing 

Distinct3
Distinct (%)1.9%
Missing14
Missing (%)8.3%
Memory size1.4 KiB
Extremadamente deteriorada
92 
Muy deteriorada
60 
Deteriorada
 
2

Length

Max length26
Median length26
Mean length21.519481
Min length11

Characters and Unicode

Total characters3314
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMuy deteriorada
2nd rowExtremadamente deteriorada
3rd rowMuy deteriorada
4th rowMuy deteriorada
5th rowExtremadamente deteriorada

Common Values

ValueCountFrequency (%)
Extremadamente deteriorada 92
54.8%
Muy deteriorada 60
35.7%
Deteriorada 2
 
1.2%
(Missing) 14
 
8.3%

Length

2024-10-30T18:54:24.677041image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-30T18:54:24.929523image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
deteriorada 154
50.3%
extremadamente 92
30.1%
muy 60
 
19.6%

Most occurring characters

ValueCountFrequency (%)
e 584
17.6%
a 492
14.8%
r 400
12.1%
d 398
12.0%
t 338
10.2%
m 184
 
5.6%
i 154
 
4.6%
o 154
 
4.6%
152
 
4.6%
E 92
 
2.8%
Other values (6) 366
11.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 584
17.6%
a 492
14.8%
r 400
12.1%
d 398
12.0%
t 338
10.2%
m 184
 
5.6%
i 154
 
4.6%
o 154
 
4.6%
152
 
4.6%
E 92
 
2.8%
Other values (6) 366
11.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 584
17.6%
a 492
14.8%
r 400
12.1%
d 398
12.0%
t 338
10.2%
m 184
 
5.6%
i 154
 
4.6%
o 154
 
4.6%
152
 
4.6%
E 92
 
2.8%
Other values (6) 366
11.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 584
17.6%
a 492
14.8%
r 400
12.1%
d 398
12.0%
t 338
10.2%
m 184
 
5.6%
i 154
 
4.6%
o 154
 
4.6%
152
 
4.6%
E 92
 
2.8%
Other values (6) 366
11.0%

Interactions

2024-10-30T18:53:56.795410image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:25.789129image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:28.554394image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:31.676266image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:34.777130image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:37.967021image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:41.159409image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:44.274712image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:47.162813image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:50.590664image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:53.953745image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:57.040751image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:25.995791image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:28.746539image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:32.140621image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:35.096320image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:38.350017image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:41.393424image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:44.540869image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:47.378570image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:51.131617image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:54.249446image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:57.240309image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:26.163753image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:28.973809image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:32.465219image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:35.410303image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:38.619052image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:41.649493image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:44.815004image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:47.656028image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:51.371534image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:54.504481image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:57.435533image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:26.429971image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:29.203223image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:32.812197image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:35.672556image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:38.896409image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:41.919992image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:45.178852image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:47.974692image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:51.557526image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:54.724295image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:57.643900image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:26.657554image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:29.480938image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:33.048658image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:35.989812image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:39.111721image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:42.192804image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:45.394507image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:48.212867image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:51.758919image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:55.098003image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:58.005995image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:27.064254image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:29.798417image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:33.299481image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:36.248882image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:39.397647image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:42.467768image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:45.634466image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:48.574447image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:52.007143image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:55.400587image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:58.263867image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:27.310000image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:30.084887image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:33.533175image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:36.694388image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:39.721436image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:42.745801image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:45.897685image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:49.092620image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:52.226707image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:55.662402image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:58.489130image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:27.547945image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:30.344994image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:33.746036image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:36.978873image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:39.975734image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:43.110981image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:46.163069image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:49.320075image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:52.526972image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:55.890360image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:58.711221image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:27.796516image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:30.589197image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:33.977006image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:37.247419image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:40.261670image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:43.351974image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:46.459070image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:49.644990image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:52.840745image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:56.130590image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:58.924693image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:28.013386image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:30.827069image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:34.212240image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:37.502701image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:40.557795image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:43.693610image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:46.677843image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:49.886128image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:53.162514image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:56.362855image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:59.190342image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:28.298227image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:31.349408image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:34.476585image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:37.723730image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:40.951965image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:43.941390image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:46.922648image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:50.353550image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:53.528040image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T18:53:56.579388image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-10-30T18:54:25.183745image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
añocalidad_de_aguacampañaclorofila_a_ug_lcodigocolorcr_total_mg_ldbo_mg_ldqo_mg_lespumasfechaicamat_suspmicrocistina_ug_lodoloresphsitiostem_aguatem_aireturbiedad_ntu
año1.0001.0000.9911.0000.0000.2511.0001.0001.0000.2890.9881.0000.2021.0001.0000.2551.0000.0001.0001.0001.000
calidad_de_agua1.0001.0000.1350.0000.2580.1350.1890.0350.0000.1440.1100.9770.1940.0000.1670.1960.0000.2580.1800.1000.000
campaña0.9910.1351.0000.3330.0000.2250.4610.2050.3770.3200.9970.2870.2490.3870.1300.2780.1060.0000.6770.5360.317
clorofila_a_ug_l1.0000.0000.3331.0000.0000.000-0.4950.5270.2610.0000.244-0.5100.0000.3160.2010.0000.1870.000-0.5210.003-0.119
codigo0.0000.2580.0000.0001.0000.4320.0000.1340.0000.4810.0000.0000.4251.0000.1620.3780.1761.0000.0000.0000.066
color0.2510.1350.2250.0000.4321.0000.0730.0000.0000.8390.2260.1610.6600.2000.2590.8250.0310.4320.1340.3870.087
cr_total_mg_l1.0000.1890.461-0.4950.0000.0731.0000.2140.0920.1140.461-0.0700.118NaN0.1430.094-0.0770.0000.127-0.491-0.060
dbo_mg_l1.0000.0350.2050.5270.1340.0000.2141.0000.1960.0000.205-0.4760.0001.0000.2160.0440.2530.134-0.233-0.229-0.254
dqo_mg_l1.0000.0000.3770.2610.0000.0000.0920.1961.0000.0000.377-0.2770.0000.5050.0360.0000.0420.000-0.519-0.4550.249
espumas0.2890.1440.3200.0000.4810.8390.1140.0000.0001.0000.3220.0860.6981.0000.1590.8510.0000.4810.2520.4140.165
fecha0.9880.1100.9970.2440.0000.2260.4610.2050.3770.3221.0000.2340.2660.3870.0890.2770.1060.0000.5830.5990.250
ica1.0000.9770.287-0.5100.0000.161-0.070-0.476-0.2770.0860.2341.0000.2000.1180.2580.2780.0290.0000.3200.0440.246
mat_susp0.2020.1940.2490.0000.4250.6600.1180.0000.0000.6980.2660.2001.0000.7750.2850.6740.2000.4250.2200.3350.150
microcistina_ug_l1.0000.0000.3870.3161.0000.200NaN1.0000.5051.0000.3870.1180.7751.000-0.1261.0000.4061.000-0.0540.091-0.072
od1.0000.1670.1300.2010.1620.2590.1430.2160.0360.1590.0890.2580.285-0.1261.0000.1520.6050.162-0.279-0.1470.222
olores0.2550.1960.2780.0000.3780.8250.0940.0440.0000.8510.2770.2780.6741.0000.1521.0000.2340.3780.0240.4570.194
ph1.0000.0000.1060.1870.1760.031-0.0770.2530.0420.0000.1060.0290.2000.4060.6050.2341.0000.176-0.243-0.1710.025
sitios0.0000.2580.0000.0001.0000.4320.0000.1340.0000.4810.0000.0000.4251.0000.1620.3780.1761.0000.0000.0000.066
tem_agua1.0000.1800.677-0.5210.0000.1340.127-0.233-0.5190.2520.5830.3200.220-0.054-0.2790.024-0.2430.0001.0000.713-0.070
tem_aire1.0000.1000.5360.0030.0000.387-0.491-0.229-0.4550.4140.5990.0440.3350.091-0.1470.457-0.1710.0000.7131.000-0.154
turbiedad_ntu1.0000.0000.317-0.1190.0660.087-0.060-0.2540.2490.1650.2500.2460.150-0.0720.2220.1940.0250.066-0.070-0.1541.000

Missing values

2024-10-30T18:53:59.606623image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-30T18:54:01.031386image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-10-30T18:54:01.893232image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

sitioscodigofechaañocampañatem_aguatem_aireodpholorescolorespumasmat_suspcolif_fecales_ufc_100mlescher_coli_ufc_100mlenteroc_ufc_100mlnitrato_mg_lnh4_mg_lp_total_l_mg_lfosf_ortofos_mg_ldbo_mg_ldqo_mg_lturbiedad_ntuhidr_deriv_petr_ug_lcr_total_mg_lcd_total_mg_lclorofila_a_ug_lmicrocistina_ug_licacalidad_de_agua
0Canal Villanueva y Río LujánTI00123/2/20222022Verano24.523.35.306.56FalseFalseFalseTrue22001001302.90.420.230.156.229.090.0NaNNaNNaNNaNNaN55.0Muy deteriorada
1Río Lujan y Arroyo CaraguatáTI00623/2/20222022Verano25.423.32.256.56TrueTrueFalseFalse12002004003.30.510.410.355.829.034.0NaNNaNNaNNaNNaN42.0Extremadamente deteriorada
2Canal Aliviador y Río LujanTI00223/2/20222022Verano24.623.32.946.59FalseTrueFalseFalse18002005806.50.050.590.541.929.017.0NaNNaNNaNNaN0.245.0Muy deteriorada
3Río Carapachay y Arroyo Gallo FiambreTI00323/2/20222022Verano25.223.32.227.45TrueTrueFalseFalse14001003007.410.380.45.829.023.0NaNNaNNaNNaNNaN46.0Muy deteriorada
4Río Reconquista y Río LujanTI00423/2/20222022Verano24.120.01.026.39FalseTrueFalseTrue11001003708.80.0490.550.542.659.018.0NaNNaNNaNNaNNaN44.0Extremadamente deteriorada
5Rio Tigre 100m antes del Rio LujánTI00523/2/20222022Verano24.923.33.506.53FalseFalseFalseTrue32002007504.43.51.10.913.9130.08.9NaNNaNNaNNaNNaN40.0Extremadamente deteriorada
6Río Lujan y Canal San FernandoTI00723/2/20222022Verano24.520.01.506.54FalseTrueFalseTrue1800015001005.620.730.63.542.012.0NaNNaNNaNNaN0.435.0Extremadamente deteriorada
7Río Capitán y Río San AntonioTI00823/2/20222022Verano24.521.06.306.48FalseTrueFalseFalse100020012003.10.0490.170.165.569.090.0NaNNaNNaNNaNNaN46.0Muy deteriorada
8Arroyo Abra Vieja y Santa RosaTI00923/2/20222022Verano23.421.04.496.76FalseFalseFalseFalse4001002201.90.10.210.191.929.039.0NaNNaNNaNNaNNaN58.0Muy deteriorada
9Del ArcaSF01523/2/20222022Verano21.523.03.856.66FalseFalseFalseTrue22001002705.40.0490.280.391.929.028.0NaNNaNNaNNaNNaN51.0Muy deteriorada
sitioscodigofechaañocampañatem_aguatem_aireodpholorescolorespumasmat_suspcolif_fecales_ufc_100mlescher_coli_ufc_100mlenteroc_ufc_100mlnitrato_mg_lnh4_mg_lp_total_l_mg_lfosf_ortofos_mg_ldbo_mg_ldqo_mg_lturbiedad_ntuhidr_deriv_petr_ug_lcr_total_mg_lcd_total_mg_lclorofila_a_ug_lmicrocistina_ug_licacalidad_de_agua
158Boca Cerrada (Res.Nat. Punta Lara)EN-extra31/10/20222022PrimaveraNaN10.0NaNNaNFalseFalseFalseFalse150100306.20.140.600.38NaN72.039.0NaN10.0NaN74.2NaN41.0Extremadamente deteriorada
159Camping Eva PerónEN08131/10/20222022Primavera16.07.011.05NaNFalseFalseFalseFalse21080905.90.150.360.21NaN33.031.0NaNNaNNaN36.50.1938.0Extremadamente deteriorada
160Toma de agua Club de PescaEN08231/10/20222022Primavera17.26.08.38NaNFalseFalseFalseFalse9535505.70.410.290.23NaN46.026.0NaNNaNNaN29.4NaN41.0Extremadamente deteriorada
161Arroyo El GatoEN08331/10/02022022Primavera18.04.07.36NaNFalseFalseFalseFalse8007002205.42.30.360.23NaNNaN23.0NaNNaNNaN16.7NaN37.0Extremadamente deteriorada
162Ensenada Prefectura Isla SantiagoEN08431/10/02022022Primavera17.15.08.98NaNFalseFalseFalseFalse13030456.10.40.240.24NaNNaN39.0NaNNaNNaN0.6NaN54.0Muy deteriorada
163Balneario Palo BlancoBS09231/10/20222022Primavera10.012.0NaNNaNFalseFalseFalseTrue8006004006.90.380.240.24NaNNaN23.0NaNNaNNaN2.1NaN43.0Extremadamente deteriorada
164Diagonal 66 (descarga cloaca)BS09531/10/20222022Primavera10.012.0NaNNaNFalseTrueFalseTrue8000080000120005.21.230.120.39NaN31.018.2NaNNaNNaN20.2NaN37.0Extremadamente deteriorada
165Playa La BagliardiBS09131/10/20222022Primavera10.012.0NaNNaNFalseFalseFalseTrue140010003804.60.80.450.43NaNNaN40.0NaNNaNNaN0.2NaN49.0Muy deteriorada
166Balneario MunicipalBS09431/10/20222022Primavera10.012.0NaNNaNFalseFalseFalseTrue180015005005.20.550.270.27NaN39.090.0NaN5.0NaN10.5NaN39.0Extremadamente deteriorada
167Playa La BalandraBS09331/10/20222022Primavera10.012.0NaNNaNFalseFalseFalseTrue9006004805.10.210.480.35NaNNaN70.0NaN5.0NaN48.0NaN34.0Extremadamente deteriorada